
'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__ :			Tuple											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Union[str, Any]											=		"▁"

UpperCAmelCase__ :			List[str]											=		{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__ :			Union[str, Any]											=		{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase__ :			Optional[Any]											=		{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
UpperCAmelCase__ :			Tuple											=		["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  VOCAB_FILES_NAMES
	snake_case__   :str   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Any   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Tuple   =  ['input_ids', 'attention_mask']

	snake_case__   :List[int]   =  []
	snake_case__   :List[int]   =  []





	def __init__(       self      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Optional[int]="</s>"   ,				__magic_name__      :							List[Any]="</s>"   ,				__magic_name__      :							List[Any]="<s>"   ,				__magic_name__      :							Tuple="<unk>"   ,				__magic_name__      :							List[Any]="<pad>"   ,				__magic_name__      :							List[Any]="<mask>"   ,				__magic_name__      :							Optional[Dict[str, Any]] = None   ,				**__magic_name__      :							List[Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				AddedToken(__magic_name__   ,				lstrip=__magic_name__   ,				rstrip=__magic_name__				) if isinstance(__magic_name__   ,				__magic_name__				) else mask_token

				lowerCAmelCase__										=				{} if sp_model_kwargs is None else sp_model_kwargs

				lowerCAmelCase__										=				kwargs.get("additional_special_tokens"   ,				[]				)
				kwargs["additional_special_tokens"] += [
				    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=__magic_name__   ,				tgt_lang=__magic_name__   ,				eos_token=__magic_name__   ,				unk_token=__magic_name__   ,				sep_token=__magic_name__   ,				cls_token=__magic_name__   ,				pad_token=__magic_name__   ,				mask_token=__magic_name__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__magic_name__   ,				)

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(str(__magic_name__				)				)
				lowerCAmelCase__										=				vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowerCAmelCase__										=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowerCAmelCase__										=				1

				lowerCAmelCase__										=				len(self.sp_model				)
				lowerCAmelCase__										=				{
				    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__magic_name__				)
				}
				lowerCAmelCase__										=				{v: k for k, v in self.lang_code_to_id.items()}
				lowerCAmelCase__										=				len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

				self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
				lowerCAmelCase__										=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

				lowerCAmelCase__										=				src_lang if src_lang is not None else "en_XX"
				lowerCAmelCase__										=				self.lang_code_to_id[self._src_lang]
				lowerCAmelCase__										=				tgt_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self._src_lang





	@src_lang.setter
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				new_src_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	def __getstate__(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.__dict__.copy()
				lowerCAmelCase__										=				None
				return state





	def __setstate__(       self      :							List[Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				d

				# for backward compatibility
				if not hasattr(self   ,				"sp_model_kwargs"				):
							lowerCAmelCase__										=				{}

				lowerCAmelCase__										=				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
				self.sp_model.Load(self.vocab_file				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				{self.convert_ids_to_tokens(__magic_name__				): i for i in range(self.vocab_size				)}
				vocab.update(self.added_tokens_encoder				)
				return vocab





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							str				):


				"""simple docstring"""

				return self.sp_model.encode(__magic_name__   ,				out_type=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				lowerCAmelCase__										=				self.sp_model.PieceToId(__magic_name__				)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							int				):


				"""simple docstring"""

				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				[]
				lowerCAmelCase__										=				""
				lowerCAmelCase__										=				False
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										if not prev_is_special:
													out_string += " "
										out_string += self.sp_model.decode(__magic_name__				) + token
										lowerCAmelCase__										=				True
										lowerCAmelCase__										=				[]
							else:
										current_sub_tokens.append(__magic_name__				)
										lowerCAmelCase__										=				False
				out_string += self.sp_model.decode(__magic_name__				)
				return out_string.strip()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

				if not os.path.isdir(__magic_name__				):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
							return
				lowerCAmelCase__										=				os.path.join(
				    __magic_name__   ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

				if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.vocab_file				):
							copyfile(self.vocab_file   ,				__magic_name__				)
				elif not os.path.isfile(self.vocab_file				):
							with open(__magic_name__   ,				"wb"				) as fi:
										lowerCAmelCase__										=				self.sp_model.serialized_model_proto()
										fi.write(__magic_name__				)

				return (out_vocab_file,)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None   ,				__magic_name__      :							bool = False				):


				"""simple docstring"""

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__magic_name__   ,				token_ids_a=__magic_name__   ,				already_has_special_tokens=__magic_name__				)

				lowerCAmelCase__										=				[1] * len(self.prefix_tokens				)
				lowerCAmelCase__										=				[1] * len(self.suffix_tokens				)
				if token_ids_a is None:
							return prefix_ones + ([0] * len(__magic_name__				)) + suffix_ones
				return prefix_ones + ([0] * len(__magic_name__				)) + ([0] * len(__magic_name__				)) + suffix_ones





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str]   ,				__magic_name__      :							Optional[str]   ,				**__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				self(__magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)
				lowerCAmelCase__										=				self.convert_tokens_to_ids(__magic_name__				)
				lowerCAmelCase__										=				tgt_lang_id
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							str = "en_XX"   ,				__magic_name__      :							Optional[List[str]] = None   ,				__magic_name__      :							str = "ro_RO"   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				tgt_lang
				return super().prepare_seqaseq_batch(__magic_name__   ,				__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return self.set_src_lang_special_tokens(self.src_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return self.set_tgt_lang_special_tokens(self.tgt_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.lang_code_to_id[src_lang]
				lowerCAmelCase__										=				[self.cur_lang_code_id]
				lowerCAmelCase__										=				[self.eos_token_id]







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.lang_code_to_id[tgt_lang]
				lowerCAmelCase__										=				[self.cur_lang_code_id]
				lowerCAmelCase__										=				[self.eos_token_id]






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int = 128   ,   lowerCamelCase_						:						int = 256   ,   lowerCamelCase_						:						float = 2_0_0_0.0   ,   lowerCamelCase_						:						int = 768   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 64   ,   lowerCamelCase_						:						int = 2048   ,   lowerCamelCase_						:						float = 0.1   ,   ):





    """simple docstring"""


    super().__init__()

    UpperCamelCase			  =      nn.Sequential(
        nn.Linear(lowerCamelCase_   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   )

    UpperCamelCase			  =      nn.Embedding(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      False

    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)

    UpperCamelCase			  =      nn.ModuleList()
    for lyr_num in range(lowerCamelCase_				):
     # FiLM conditional T5 decoder
     UpperCamelCase			  =      DecoderLayer(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
     self.decoders.append(lowerCamelCase_				)

    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.mul(query_input.unsqueeze(-1				)   ,   key_input.unsqueeze(-2				)				)
    return mask.unsqueeze(-3				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCamelCase			  =      get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype				)

    UpperCamelCase			  =      self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCamelCase			  =      decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCamelCase			  =      torch.broadcast_to(
        torch.arange(lowerCamelCase_   ,   device=decoder_input_tokens.device				)   ,   (batch, seq_length)   ,   )

    UpperCamelCase			  =      self.position_encoding(lowerCamelCase_				)

    UpperCamelCase			  =      self.continuous_inputs_projection(lowerCamelCase_				)
    inputs += position_encodings
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    # decoder: No padding present.
    UpperCamelCase			  =      torch.ones(
        decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype				)

    # Translate encoding masks to encoder-decoder masks.
    UpperCamelCase			  =      [(x, self.encoder_decoder_mask(lowerCamelCase_   ,   lowerCamelCase_				)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCamelCase			  =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1				)
    UpperCamelCase			  =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1				)

    for lyr in self.decoders:
     UpperCamelCase			  =      lyr(
         lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      self.decoder_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.post_dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.spec_out(lowerCamelCase_				)
    return spec_out





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Any=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)				)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_   ,   )				)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer[0](
        lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    if encoder_hidden_states is not None:
     UpperCamelCase			  =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1E10				).to(
         encoder_hidden_states.dtype				)

     UpperCamelCase			  =      self.layer[1](
         lowerCamelCase_   ,   key_value_states=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    # Apply Film Conditional Feed Forward layer
    UpperCamelCase			  =      self.layer[-1](lowerCamelCase_   ,   lowerCamelCase_				)

    return (hidden_states,)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)

    if conditioning_emb is not None:
     UpperCamelCase			  =      self.FiLMLayer(lowerCamelCase_   ,   lowerCamelCase_				)

    # Self-attention block
    UpperCamelCase			  =      self.attention(lowerCamelCase_				)

    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)

    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.attention(
        lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   attention_mask=attention_mask.squeeze(1				)   ,   )
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return layer_output





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaDenseGatedActDense(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    if conditioning_emb is not None:
     UpperCamelCase			  =      self.film(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.DenseReluDense(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)
    UpperCamelCase			  =      NewGELUActivation()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.act(self.wi_a(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.wi_a(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_gelu * hidden_linear
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.wo(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Parameter(torch.ones(lowerCamelCase_				)				)
    UpperCamelCase			  =      eps







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_states.to(torch.floataa				).pow(2				).mean(-1   ,   keepdim=lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCamelCase			  =      hidden_states.to(self.weight.dtype				)

    return self.weight * hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase_   ,   3.0				))				))





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   out_features * 2   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scale_bias(lowerCamelCase_				)
    UpperCamelCase  ,     UpperCamelCase			  =      torch.chunk(lowerCamelCase_   ,   2   ,   -1				)
    UpperCamelCase			  =      x * (1 + scale) + shift
    return x
